Evidence (13827 claims)
Adoption
8454 claims
Productivity
7544 claims
Governance
6789 claims
Human-AI Collaboration
6327 claims
Org Design
4126 claims
Innovation
4058 claims
Labor Markets
3520 claims
Skills & Training
2924 claims
Inequality
2057 claims
Evidence Matrix
Claim counts by outcome category and direction of finding.
| Outcome | Positive | Negative | Mixed | Null | Total |
|---|---|---|---|---|---|
| Other | 749 | 195 | 97 | 889 | 1979 |
| Governance & Regulation | 815 | 391 | 188 | 121 | 1539 |
| Organizational Efficiency | 771 | 189 | 124 | 83 | 1177 |
| Technology Adoption Rate | 624 | 233 | 123 | 96 | 1084 |
| Research Productivity | 410 | 121 | 56 | 331 | 929 |
| Output Quality | 466 | 177 | 59 | 47 | 749 |
| Decision Quality | 320 | 174 | 75 | 42 | 618 |
| Firm Productivity | 435 | 55 | 88 | 20 | 604 |
| AI Safety & Ethics | 214 | 276 | 65 | 33 | 593 |
| Market Structure | 178 | 166 | 122 | 24 | 495 |
| Task Allocation | 206 | 64 | 70 | 31 | 376 |
| Skill Acquisition | 165 | 57 | 60 | 17 | 299 |
| Innovation Output | 201 | 27 | 41 | 18 | 288 |
| Employment Level | 105 | 51 | 107 | 13 | 278 |
| Fiscal & Macroeconomic | 131 | 69 | 43 | 26 | 276 |
| Consumer Welfare | 116 | 63 | 42 | 11 | 232 |
| Firm Revenue | 149 | 46 | 26 | 3 | 224 |
| Inequality Measures | 44 | 122 | 49 | 6 | 221 |
| Task Completion Time | 169 | 29 | 8 | 12 | 219 |
| Worker Satisfaction | 89 | 61 | 20 | 12 | 182 |
| Error Rate | 69 | 91 | 10 | 2 | 172 |
| Regulatory Compliance | 76 | 68 | 14 | 5 | 163 |
| Training Effectiveness | 92 | 19 | 13 | 19 | 145 |
| Wages & Compensation | 77 | 36 | 25 | 6 | 144 |
| Automation Exposure | 51 | 54 | 22 | 12 | 142 |
| Team Performance | 86 | 17 | 27 | 9 | 140 |
| Developer Productivity | 94 | 17 | 14 | 6 | 132 |
| Job Displacement | 12 | 80 | 20 | 1 | 113 |
| Hiring & Recruitment | 51 | 7 | 8 | 3 | 69 |
| Skill Obsolescence | 5 | 45 | 6 | 1 | 57 |
| Creative Output | 31 | 16 | 7 | 2 | 57 |
| Social Protection | 27 | 16 | 8 | 2 | 53 |
| Labor Share of Income | 17 | 17 | 17 | — | 51 |
| Worker Turnover | 11 | 12 | — | 3 | 26 |
| Industry | — | — | — | 1 | 1 |
Heterogeneity analyses reveal stronger effects of digital technologies on visibility and resilience in high-complexity supply chains.
Reported heterogeneity/subgroup analyses in the paper (no subgroup sample sizes provided in the excerpt); methods include regression/SEM.
Supply chain visibility mediates 67.4% of the total effect of digital technologies on supply chain resilience (mediation = 67.4%; bootstrap CI [0.156, 0.253]; Sobel test Z = 8.745, p< .001).
Mediation analysis reported in the paper using bootstrapped confidence intervals and a Sobel test; sample of 742 firms.
Supply chain visibility significantly predicts supply chain resilience (= 0.486, p< .001).
SEM / regression coefficient reported in paper with p-value (< .001); sample of 742 firms.
Digital technologies (IoT, blockchain, AI, big data analytics, and cloud computing) exert a significant positive effect on supply chain resilience (= 0.298, p< .001).
Hierarchical regression and SEM results reported in the paper; sample of 742 firms; p-value reported (< .001).
Digital technologies (IoT, blockchain, AI, big data analytics, and cloud computing) exert a significant positive effect on supply chain visibility (= 0.412, p< .001).
Hierarchical regression and SEM results reported in the paper; sample of 742 firms; p-value reported (< .001).
The results demonstrate a 'less is more' pattern: simpler combination (memory + reflection) yields better performance than adding architectural complexity.
Authors' interpretation of the ablation study results showing that adding multiple extra mechanisms degraded performance compared to the memory+reflection configuration.
A nine-variant ablation reveals that memory and reflection together produce a 58% cumulative improvement over the stateless baseline.
Ablation study with nine variants on the sequential portfolio benchmark; authors report a 58% cumulative improvement when combining memory and reflection versus the stateless baseline.
AEL outperforms five published self-improving methods and all non-LLM baselines while maintaining the lowest variance among all LLM-based approaches on the benchmark.
Comparative empirical evaluation on the same sequential portfolio benchmark, comparing AEL to five published self-improving methods and multiple non-LLM and LLM baselines (reported relative ranking and variance).
On a sequential portfolio benchmark (10 sector-diverse tickers, 208 episodes, 5 random seeds), AEL achieves a Sharpe ratio of 2.13 ± 0.47.
Empirical experiment on the sequential portfolio benchmark with 10 tickers, 208 episodes, evaluated across 5 random seeds (reported Sharpe ratio and standard deviation).
We introduce Agent Evolving Learning (AEL), a two-timescale framework in which a Thompson Sampling bandit at the fast timescale learns which memory retrieval policy to apply each episode, while LLM-driven reflection at the slow timescale diagnoses failure patterns and injects causal insights into the agent's decision prompt.
Methodological description and proposed algorithmic design in the paper (no additional experimental sample size—design/algorithmic claim).
The sustainability of the algorithmic state rests on a movement from technocratic secrecy to value-based transparency to ensure AI- and human collaboration is founded on institutional accountability and algorithmic justice.
Authorial conclusion from the systematic review synthesis (2018-2026) advocating a policy/practice shift; presented as normative policy recommendation rather than quantified empirical finding.
Empirical evidence shows great gains in efficiency in fiscal forecasting.
Empirical studies included in the PRISMA-guided review (2018-2026) reporting improved fiscal forecasting outcomes; no quantitative effect sizes provided in abstract.
Empirical evidence shows great gains in efficiency at routinised administrative tasks.
Empirical studies reported in the systematic review (2018-2026); the abstract claims empirical evidence of efficiency gains but does not report specific study counts, sample sizes, or effect magnitudes.
Digital infrastructure is a primary determinant of both the pace of AI diffusion and its resulting economic returns.
Synthesis of descriptive patterns, difference-in-differences causal estimates, and instrumental-variable results using Turkish administrative and survey data (2021-2024).
Infrastructure-driven AI adoption shifts labor composition toward ICT-related roles.
Instrumental-variable estimates showing changes in occupational composition (increase in ICT-related roles) associated with infrastructure-driven AI adoption; based on administrative employment data and enterprise survey (Turkey, 2021-2024).
Infrastructure-driven AI adoption raises export intensity.
Instrumental-variable estimates linking infrastructure-driven adoption to firm export intensity using administrative and survey data (Turkey, 2021-2024).
Infrastructure-driven AI adoption raises labor productivity.
Instrumental-variable estimates where infrastructure-driven adoption is instrumented (IV) and linked to firm-level labor productivity measures; data from administrative records and enterprise survey in Turkey (2021-2024).
Improved connectivity (due to pipeline-driven fiber deployment) significantly increases AI adoption, particularly for software-intensive technologies and among small and medium-sized enterprises.
Causal inference using difference-in-differences estimates exploiting staggered pipeline expansion as variation in connectivity; sample drawn from administrative records and nationally representative enterprise survey (Turkey, 2021-2024).
AI adoption is concentrated among large firms and in regions with high-speed broadband and proximity to data centers, particularly for software-intensive and cloud-based applications.
Descriptive analysis using administrative data and a nationally representative enterprise survey from Turkey (2021-2024).
This survey provides scholars and practitioners with a structured understanding of how agentic AI is reshaping financial markets and identifies critical research directions to ensure these systems enhance both operational efficiency and market resilience.
Statement of contribution in the paper; based on the paper's literature review, taxonomy, and identified research agenda.
Agentic AI offers substantial potential for enhanced market efficiency, liquidity provision, and risk management.
Survey synthesis of foundational research, market applications, and technical architectures suggesting potential benefits; no original empirical evaluation reported.
The emergence of agentic AI represents a fundamental transformation in financial markets, characterized by autonomous systems capable of reasoning, planning, and adaptive decision-making with minimal human intervention.
Conceptual claim stated in the survey's introduction and synthesis of recent advances; based on literature review and theoretical framing rather than new empirical data.
Countries around the world are rushing to encourage greater investment and growth in their domestic AI industries.
Statement/observation presented in the paper's introduction; based on the paper's descriptive overview of global policy activity (literature review / policy survey implied). No sample size reported.
Results indicate that AI-assisted text-to-model methods can substantially lower the cost of constructing structured procedural representations, making course-wide deployment of structured AI coaching systems practically feasible.
Conclusion drawn from reported results (e.g., time reductions and modeled outputs); the paper claims that these results imply lower costs and practical feasibility for course-wide deployment.
AI-assisted authoring reduced expert modeling time by 50–70% while producing structurally valid and highly reproducible models under fixed-input conditions.
Quantitative claim reported in the paper comparing expert modeling time with AI assistance and reporting structural validity and reproducibility under fixed-input conditions; exact experimental setup and sample size not stated in the abstract.
We apply the pipeline to instructional materials from a graduate-level online AI course, constructing 23 procedural skill models.
Empirical application reported in the paper: the pipeline was run on course materials and produced 23 models (number explicitly stated).
The approach automates structural scaffolding while preserving expert oversight for validating causal transitions and failure conditions.
Claim about system design and human-in-the-loop workflow reported in the paper; implies human validation steps are maintained alongside automated generation.
We present a human-in-the-loop text-to-model pipeline that uses large language models to transform instructional materials into schema-complete Task-Method-Knowledge models via ontology-constrained prompting and template-based generation.
Methodological contribution described in the paper; pipeline design and implementation reported (no separate quantitative validation in this sentence).
When unfairness is driven by uncertainty (rather than incidental noise), accounting for uncertainty is essential to achieving fair and effective decision-making.
Synthesis/argument based on formalization and simulation experiments showing cases where uncertainty causes unfair outcomes and methods that account for uncertainty mitigate those outcomes.
The proposed framework can help practitioners diagnose, audit, and govern fairness risks in socio-technical decision systems.
Authors propose a diagnostic/audit/governance framework (conceptual contribution) and illustrate its use through examples and simulations; no field deployment evidence provided in the abstract.
Algorithmic examples in the paper demonstrate it is possible to reduce outcome variance for disadvantaged groups while preserving institutional objectives such as expected utility.
Algorithmic examples and simulation experiments reported in the paper demonstrating reductions in outcome variance for disadvantaged groups together with preserved expected utility (results from synthetic/simulated data and model runs).
The authors formalize model and feedback uncertainty using counterfactual logic and reinforcement learning.
Paper describes formalization/mathematical definitions linking counterfactual logic and reinforcement learning to model and feedback uncertainty (theoretical/methodological contribution).
This paper introduces a taxonomy of uncertainty in sequential decision-making consisting of three types: model uncertainty, feedback uncertainty, and prediction uncertainty.
Paper presents a conceptual taxonomy and names the three uncertainty types in the text/abstract; theoretical exposition in the methods/definitions sections (no external empirical sample required).
The emergence of 'Industry 4.0 Inc.' is likely to induce further collaboration among participating incumbents.
Authors' inference based on observed interconnections and overlapping investments in the M&A-based mapping (predictive/interpretive claim; no quantified projection provided in the excerpt).
One consequence of increased M&A activity and overlapping investments is the emergence of interconnections that have given rise to a new structure the authors term 'Industry 4.0 Inc.'
Network mapping of corporate linkages and overlapping investments derived from the M&A deal analysis spanning more than two decades (method: empirical mapping of inter-corporate ties); exact counts not provided in the excerpt.
Mergers and acquisitions are one of the principal tools industrial firms use to overcome this dual challenge.
Authors' argumentation supported by an empirical analysis of more than two decades of M&A deals (method: M&A deal analysis); exact sample size not stated in provided text.
HAF-DS provides a scalable and adaptable solution for modern textile and PPE supply chains.
Author claim in conclusions indicating scalability and adaptability as properties of the proposed framework; supported implicitly by application to multiple datasets.
Coupling predictive forecasting with prescriptive optimization enhances both accuracy and efficiency in textile and PPE supply chains.
Summary conclusion drawn from the reported experimental improvements in forecast errors and operational metrics on textile and PPE datasets.
Service level rose from 95.5% to 97.8%.
Reported experimental operational metric (service level) improvement values under HAF-DS versus baseline (95.5% -> 97.8%).
Stockouts decreased by 27.5%.
Reported experimental operational metric indicating a 27.5% reduction in stockouts under HAF-DS compared to baseline.
Inventory cost decreased by 5.4%.
Reported experimental operational metric (inventory cost) showing 5.4% reduction under HAF-DS relative to baseline.
On the combined dataset, HAF-DS reduced Mean Absolute Percentage Error (MAPE) from 9.5% to 8.1%.
Reported experimental result on the combined dataset comparing MAPE of HAF-DS vs baseline (values given: 9.5% -> 8.1%).
On the combined dataset, HAF-DS reduced Root Mean Squared Error (RMSE) from 19.53 to 17.11 (12.4%).
Reported experimental result on the combined dataset comparing RMSE of HAF-DS vs baseline (values given: 19.53 -> 17.11, with percent reduction 12.4%).
On the combined dataset, HAF-DS reduced Mean Absolute Error (MAE) from 15.04 to 12.83 (14.7%).
Reported experimental result on the combined dataset comparing MAE of HAF-DS vs baseline (values given: 15.04 -> 12.83, with percent reduction 14.7%).
Experiments on textile sales and supply chain datasets show significant performance gains over statistical and deep learning baselines.
Empirical evaluation reported on textile sales and supply chain datasets with comparisons to statistical and deep learning baseline models (datasets described broadly; no sample sizes given).
The framework jointly minimizes forecasting error and operational cost through embedding-based feature representation and recurrent neural architectures.
Paper text describing joint objective (minimize forecasting error and operational cost) and the use of embedding-based features plus recurrent networks to accomplish this.
The optimization layer prescribes cost-efficient replenishment and allocation decisions (MILP).
Method description stating the use of a MILP optimization layer to produce replenishment/allocation decisions aimed at cost efficiency.
The LSTM captures temporal and contextual demand dependencies.
Methodological description asserting LSTM's role in modeling temporal and contextual dependencies within the forecasting module.
The paper proposes a Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)-based demand forecasting module with a mixed integer linear programming (MILP) optimization layer.
Paper description of the proposed framework (design/architecture). Reports integration of LSTM forecasting module and MILP optimization layer as the core contribution.
Dynamic combinations of AI and organizational structure can help managers overcome traditional trade-offs between scale and scope, opening pathways for scalable, cross-market expansion.
Managerial implication drawn from the paper's longitudinal case study of ByteDance; qualitative inference from observed organizational practices and AI deployment patterns.